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<title>Using renv with Docker</title>

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<h1 class="title toc-ignore">Using renv with Docker</h1>



<p>While <code>renv</code> can help capture the state of your R library at some point in time, there are still other aspects of the system that can influence the runtime behavior of your R application. In particular, the same R code can produce different results depending on:</p>
<ul>
<li>The operating system in use,</li>
<li>The compiler flags used when R and packages are built,</li>
<li>The LAPACK / BLAS system(s) in use,</li>
<li>The versions of system libraries installed and in use,</li>
</ul>
<p>And so on. <a href="https://www.docker.com/">Docker</a> is a tool that helps solve this problem through the use of <strong>containers</strong>. Very roughly speaking, one can think of a container as a small, self-contained system within which different applications can be run. Using Docker, one can declaratively state how a container should be built (what operating system it should use, and what system software should be installed within), and use that system to run applications. (For more details, please see <a href="https://environments.rstudio.com/docker" class="uri">https://environments.rstudio.com/docker</a>.)</p>
<p>Using Docker and <code>renv</code> together, one can then ensure that both the underlying system, alongside the required R packages, are fixed and constant for a particular application.</p>
<p>The main challenges in using Docker with <code>renv</code> are:</p>
<ul>
<li>Ensuring that the <code>renv</code> cache is visible to Docker containers, and</li>
<li>Ensuring that <code>renv</code> restores the R packages as required when the container is run.</li>
</ul>
<p>This vignette will assume you are already familiar with Docker; if you are not yet familiar with Docker, the <a href="https://docs.docker.com/">Docker Documentation</a> provides a thorough introduction. To learn more about using Docker to manage R environments, visit <a href="https://environments.rstudio.com/docker.html">environments.rstudio.com</a>. We’ll discuss two strategies for using <code>renv</code> with Docker:</p>
<ol style="list-style-type: decimal">
<li>Using <code>renv</code> to install packages when the Docker image is generated;</li>
<li>Using <code>renv</code> to install packages when Docker containers are run.</li>
</ol>
<p>We’ll explore the pros and cons of each strategy.</p>
<div id="creating-docker-images-with-renv" class="section level2">
<h2>Creating Docker Images with renv</h2>
<p>With Docker, <a href="https://docs.docker.com/engine/reference/builder/">Dockerfiles</a> are used to define new images. Dockerfiles can be used to declaratively specify how a Docker image should be created. A Docker image captures the state of a machine at some point in time – e.g., an Ubuntu operating system after downloading and installing R 3.5. Docker containers can be created using that image as a base, allowing isolated applications to run using the same pre-defined machine state.</p>
<p>First, you’ll need to get <code>renv</code> installed on your Docker image. The easiest way to accomplish this is with the <code>remotes</code> package. For example:</p>
<pre><code>ENV RENV_VERSION 0.14.0
RUN R -e &quot;install.packages(&#39;remotes&#39;, repos = c(CRAN = &#39;https://cloud.r-project.org&#39;))&quot;
RUN R -e &quot;remotes::install_github(&#39;rstudio/renv@${RENV_VERSION}&#39;)&quot;</code></pre>
<p>Now, <code>renv</code> can be used to install packages on the image. If you’d like the <code>renv.lock</code> lockfile to be used to install R packages when the Docker image is built, you can include something of the form:</p>
<pre><code>WORKDIR /project
COPY renv.lock renv.lock
RUN R -e &#39;renv::restore()&#39;</code></pre>
<p>With this, <code>renv</code> will download and install packages from CRAN and other external sources as appropriate when the image is created.</p>
<p>There are two main downsides to this approach:</p>
<ol style="list-style-type: decimal">
<li><p>The set of R packages used is pre-baked into the image, so different applications or containers built from this image will either have to re-use the aforementioned set of packages, or reinstall the packages they need to update as required.</p></li>
<li><p>With this approach, the <code>renv</code> package cache will not be used. This implies that package installation through <code>renv::restore()</code> may be very slow, as all packages will have to be installed.</p></li>
</ol>
<p>Both of these issues can be solved if package installation can be deferred to container runtime.</p>
</div>
<div id="running-docker-containers-with-renv" class="section level2">
<h2>Running Docker Containers with renv</h2>
<p>If you’d like to leverage the <code>renv</code> package cache alongside Docker, then you’ll need to alter how your containers are created so that <code>renv</code> can ensure the project library is initialized before your application is run.</p>
<p>One can control the <code>renv</code> cache directory with the environment variable <code>RENV_PATHS_CACHE</code>. For example:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">Sys.setenv</span>(<span class="at">RENV_PATHS_CACHE =</span> <span class="st">&quot;~/path/to/cache&quot;</span>)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>renv<span class="sc">:::</span><span class="fu">renv_paths_cache</span>()</span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [1] &quot;~/path/to/cache/v5/R-4.1/x86_64-apple-darwin17.0&quot;</span></span></code></pre></div>
<p>Note that the platform and R version in use are appended to the requested cache directory. This ensures that a single directory can act a base of cached packages for multiple different platforms and R versions.</p>
<p>Next, we need to figure out how to tell the Docker containers we create to use this cache. The most common option here is to mount a directory in the container that maps to persistent storage on the host system, and then set the aforementioned <code>RENV_PATHS_CACHE</code> environment variable to point to this mount. You can specify this when the container is launched. For example, if you had a container running a Shiny application:</p>
<pre><code># the path to an renv cache on the host machine
RENV_PATHS_CACHE_HOST=/opt/local/renv/cache

# the path to the cache within the container
RENV_PATHS_CACHE_CONTAINER=/renv/cacheA

# run the container with the host cache mounted in the container
docker run --rm \
    -e &quot;RENV_PATHS_CACHE=${RENV_PATHS_CACHE_CONTAINER}&quot; \
    -v &quot;${RENV_PATHS_CACHE_HOST}:${RENV_PATHS_CACHE_CONTAINER}&quot; \
    -p 14618:14618 \
    R --vanilla -s -e &#39;renv::restore(); shiny::runApp(host = &quot;0.0.0.0&quot;, port = 14618)&#39;</code></pre>
<p>With this, any calls to <code>renv</code> APIs within the created docker container will have access to the mounted cache. The first time you run a container, <code>renv</code> will likely need to populate the cache, and so some time will be spent downloading and installing the required packages. Subsequent runs should be much faster, as <code>renv</code> will be able to reuse the global package cache.</p>
<p>The primary downside with this approach compared to the image-based approach is that it requires you to modify how containers are created, and requires a bit of extra orchestration in how containers are launched. However, once the <code>renv</code> cache is active, newly-created containers will launch very quickly, and a single image can then be used as a base for a myriad of different containers and applications, each with their own private R library.</p>
</div>
<div id="handling-the-renv-autoloader" class="section level2">
<h2>Handling the renv Autoloader</h2>
<p>When is launched within a project folder, he <code>renv</code> auto-loader (if present) will attempt to download and install <code>renv</code> into the project library. Depending on how your Docker container is configured, this could fail. For example:</p>
<pre><code>Error installing renv:
======================
ERROR: unable to create ‘/usr/local/pipe/renv/library/master/R-4.0/x86_64-pc-linux-gnu/renv’
Warning messages:
1: In system2(r, args, stdout = TRUE, stderr = TRUE) :
  running command &#39;&#39;/usr/lib/R/bin/R&#39; --vanilla CMD INSTALL -l &#39;renv/library/master/R-4.0/x86_64-pc-linux-gnu&#39; &#39;/tmp/RtmpwM7ooh/renv_0.12.2.tar.gz&#39; 2&gt;&amp;1&#39; had status 1
2: Failed to find an renv installation: the project will not be loaded.
Use `renv::activate()` to re-initialize the project.</code></pre>
<p>Bootstrapping <code>renv</code> into the project library might be un-necessary for you. If that is the case, then you can avoid this behavior by launching R with the <code>--vanilla</code> flag set; for example:</p>
<pre><code>R --vanilla -s -e &#39;renv::restore()&#39;</code></pre>
</div>



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